Method of classification of tagged material in a set of tomographic images of colorectal region

ABSTRACT

A method of classification of image portions corresponding to faecal residues from a tomographic image of a colorectal region, which comprises a plurality of voxels ( 2 ) each having a predetermined intensity value and which shows at least one portion of colon ( 6   a,    6   b,    6   c,    6   d ) comprising 
     at least one area of tagged material ( 10 ). The area of tagged material ( 10 ) comprises at least one area of faecal residue ( 10   a ) and at least one area of tissue affected by tagging ( 10   b ). The image further comprises at least one area of air ( 8 ) which comprises an area of pure air ( 8   a ) not influenced by the faecal residues. The method comprises the operations of identifying ( 100 ), on the basis of a predetermined identification criterion based on the intensity values, above-threshold connected regions comprising connected voxels ( 2 ) and identifying, within the above-threshold connected regions, a plurality of connected regions of tagged material comprising voxels ( 2 ) representing the area of tagged material ( 10 ). The method further comprises the operation of classifying ( 104 ) each plurality of connected regions of tagged material on the basis of specific classification comparison criteria for each connected region, in such a way as to identify voxels ( 20 ) corresponding to the area of faecal residue ( 10   a ) and voxels ( 2 ) corresponding to the area of tissue affected by tagging ( 10   b ).

The present invention relates to a method of classification of objectsand/or structures in tomographic images, in particular ComputerisedAxial Tomography (CAT) images.

More specifically, the invention relates to a method of digitalclassification of the contents of an image of a colorectal region, asdefined in the preamble of claim 1.

Virtual colonoscopy is a technique which makes possible the earlydetection of pre-neoplastic lesions present in a colorectal regionthrough analysis of a series of tomographic images, in particularComputerised Axial Tomography (CAT) images. Each of these imagesrepresents an axial section of the patient's abdomen and corresponds tothe signal obtained from the sampled volume, which in turn depends onthe absorption of X-rays by the different tissues. The diagnosticanalysis of the set of images may be performed both via a study of theimages themselves and by carrying out virtual navigation in thepatient's organ via three-dimensional processing of the images.

In computerised axial tomography, datasets of images are generated,translating the data regarding the attenuation exerted by the tissue andby the anatomical structures on the incident X-rays into levels ofintensity of grey. Since the attenuation of the faecal materialsubjected to X-rays is similar to that of other tissues such as, inparticular, structures protruding from the colon wall and structuresconnected thereto, the pre-neoplastic lesions submerged in the faecalmaterial may not be correctly identified.

In order to solve this problem, methods of classification of the faecalresidues have been introduced which, by means of prior oraladministration to the patient of a contrast agent, exhibit greaterattenuation compared with that of the surrounding tissues, thus becomingdistinguishable. In the images acquired in this way, however, the colonwall in contact with the tagged material also undergoes a spuriousincrease in intensity.

Various methods have been proposed for classifying and digitallyremoving the faecal residues. However, such methods propose approachesbased on the analysis of the characteristics of the tagged faeces as awhole, proving to be not very robust in the case where the level oftagging varies along the colon, or by carrying out analyses based on thecomparison of the value of each individual voxel with the values of theneighbouring voxels, with the consequent need to employ significantcomputing resources.

It is therefore an object of the present invention to propose a methodof classification which is capable of classifying in a simple and rapidmanner the faecal residues present in the dataset of images of acolorectal region, even in the presence of variations in the level oftagging of the faeces.

These and other objects are achieved by a method of classificationhaving the characteristics defined in claim 1.

Particular embodiments are the subject of the dependent claims.

A processing system and a program for a processor as claimed form afurther subject of the invention.

Other features and advantages of the invention will become clear fromthe following detailed description which is given purely by way ofnon-limiting example with reference to the appended drawings, in which:

FIG. 1 is a diagrammatic representation of an image of an abdominalsection;

FIG. 2 is a first diagrammatic representation of a colon section;

FIG. 3 is a flow diagram of the method according to the invention;

FIG. 4 is a second diagrammatic representation of the colon section ofFIG. 2;

FIG. 5 is a representation of the course of the intensity between thecorrected area and the colon wall,

FIG. 6 is a representation of a straight line reconstruction of thevalues of the voxels of the area of tissue affected by tagging, and

FIG. 7 is a diagrammatic representation of a processing system for theimplementation of the method according to the invention.

In the following description, the method of classification according tothe invention will be illustrated with reference to the particular andmore usual application of a method of electronic subtraction of faecalresidues.

To be considered is an image of a dataset (set of images) acquired bymeans of a system of computerised axial tomography exploiting thedifferent absorption of X-rays by the tissues. As an alternative, it ispossible to acquire said dataset by other tomographic techniques, suchas, for example, nuclear magnetic resonance.

FIG. 1 shows in a simplified form a binary image of an abdominal sectioncomprising, in a manner known per se, a plurality of voxels, some ofwhich have been represented as minuscule squares indicated as a whole bythe reference 2. Such voxels 2 have intensity values related to theattenuation presented by the corresponding sample of the sampled volumeto X-rays. The different levels of grey of the images displayedtherefore correspond to the attenuation exhibited by the structuresrepresented. The reference 4 indicates the body of the patient and thereferences 6 a, 6 b, 6 c, 6 d indicate a plurality of portions of thecolon corresponding to different intersections of the colon with thesection plane corresponding to the image. The portions of the colon 6 a,6 b, 6 c, 6 d each comprise an area of air 8 and an area of taggedmaterial 10 rendered detectable thanks to the presence of a contrastagent previously administered to the patient.

FIG. 1 further shows portions of bone 12 corresponding for example tothe spine.

FIG. 2 is an enlarged representation of a portion of the colon 6 a, 6 b,6 c, 6 d, for example the portion 6 d, bounded by a colon wall 13. FIG.2 also shows tissue 14 surrounding said portion of the colon 6 d and notaffected by tagging, the area of air 8 and the area of tagged material10.

The area of tagged material 10 is subdivided into an area of faecalresidue 10 a and an area of tissue affected by tagging 10 b; the area ofair 8 is subdivided into an area of pure air 8 a, in which the intensityvalues of the voxels 2 are not influenced by the presence of the area oftagged material 10 and are preferably contained in the interval between−1024 HU and −850 HU, and an area of air affected by tagging 8 b, inwhich the intensity values of the voxels 2 are altered by the presenceof the area of tagged material 10.

As may be noted from FIG. 2, the area of tissue affected by tagging 10 bcomprises voxels 2 located around the colon wall 13 and influenced bythe proximity of the tagged voxels 2 of the area of faecal residue 10 a.

The area of tissue affected by tagging 10 b is constituted by voxels 2the intensity values of which are artificially hyperintense because ofthe partial volume effect due to the proximity of the voxels 2 of thearea of faecal residue 10 a and/or because of artefacts from hardeningof the beam of X-rays.

The area of faecal residue 10 a may comprise tagged faecal residue andsmall areas of untagged material, such as, for example, air bubbles,untagged faeces, etc.

In the following description of the method of the invention, all theoperations are carried out with reference to the entire dataset ofimages, i.e. they are carried out three-dimensionally.

With reference to FIG. 3, the method of the invention comprises, as afirst operation, the identification 100 of regions of connected voxels 2having intensity values incompatible with the intensity values of thevoxels 2 of the surrounding tissue 14, for example equal to −100 HU, orof the area of pure air 8 a, for example equal to −900 HU, or of areasof air outside the colon, for example, outside the patient or insidelungs, not illustrated in the drawing.

In this operation all the voxels 2 of the areas of tagged material 10and the voxels 2 of the portions of bone 12 are thus detected, since thebones also have a coefficient of attenuation comparable to that of theareas of tagged material 10. Initially, starting voxels 2, hereinaftertermed semis 16, are detected, on the basis of predetermined criteria.To groups of adjacent semis 16 are annexed voxels 2 which possessspecific properties, fixed a priori or derived from the properties ofthe group of semis 16 to which they are annexed. Advantageously, acriterion for detection of the semis 16 is based on an intensitythreshold. In particular, a first threshold of fixed value, preferablyequal to 200 HU, is established, and as semis 16 the voxels 2 having anintensity value equal to or greater than that first threshold areselected. Then, above-threshold connected regions are generatedcomprising all the voxels 2 having an intensity greater than a secondthreshold, preferably of equal value to the first threshold.

Still in the identification step 100 described above, morphologicaloperations are applied (such as, for example, dilations and erosions) inorder to delineate the shape of said above-threshold connected regions,if necessary compensating in part the effect of the noise of acquisitionof the image and artefacts due to the image itself.

These morphological operations further make it possible to add to theabove-threshold connected regions also small isolated groups ofbelow-threshold voxels 2 (representing, for example, air, particles,etc.) present in the areas of tagged material 10.

In the following step 102, among the above-threshold connected regionsdetected in the preceding step, connected regions of bone comprising thevoxels 2 which correspond to the portions of bone 12 are identified.Step 102 comprises the application of a series of morphologicaloperations to the above-threshold connected regions in such a way as toseparate the connected regions of bone from connected regions of taggedmaterial. Sometimes, in fact, because of the effect of partial volumeand of acquisition artefacts, an individual above-threshold connectedregion may be constituted in part by an area of tagged material 10,comprising the voxels 2 which correspond to the tagged material itself,and by portions of bone 12 adjacent thereto.

Hereinafter, each of the connected regions is analysed on the basis ofdata known a priori regarding the extent, the shape and the location ofthe portions of bone 12. Advantageously, the characteristics of thevoxels 2 belonging to the same connected regions of bone are furtherutilised, exploiting characteristics such as local homogeneity, standarddeviation, characteristics linked to the curvature of the surface voxels2 of the regions.

Preferably a check is made as to whether the above-threshold connectedregion starts in the first image of the colon and has a length along theaxis Z greater than half of the images which cover the entire length ofthe colon, identifying it in that case as a connected regioncorresponding to a portion of bone 12. The presupposition is in factthat the bones are a vertically connected single structure having asufficient length.

The connected regions of bone thus identified are not therefore used inthe succeeding steps of the method described hereinafter.

In the following step 104, classification is carried out for eachconnected region of tagged material, between the area of faecal residue10 a and the area of tissue affected by tagging 10 b.

The area of faecal residue 10 a and the area of tissue affected bytagging 10 b have characteristics (intensity, homogeneity, . . . ) whichdifferentiate them in the individual connected regions of taggedmaterial, but which cease to be discriminant as soon as the colon isconsidered as a whole. For example, the voxels 2 of the area of faecalresidue 10 a have on average an intensity value greater than that of thevoxels 2 of the area of tissue affected by tagging 10 b, but it mayoccur that areas of faecal residue 10 a of a connected region of taggedmaterial having a lesser presence of contrast agent exhibit a lowerintensity value than areas of tissue affected by tagging 10 b of aconnected region of tagged material in which, instead, there is agreater quantity of contrast agent.

Making a distinction between voxels 2 of faecal residue 10 a and voxels2 of tissue affected by tagging 10 b in each connected region of taggedmaterial is therefore more efficacious than carrying out theclassification with criteria based on the indistinct whole of all thevoxels 2 of the colon corresponding to faeces and tissue affected bytagging.

Alongside this, carrying out a classification at the level of theindividual connected regions of tagged material is quicker and moreefficient than making a comparison between each of the voxels and itsneighbours.

In order to carry out the classification 104 of the voxels of theconnected regions of tagged material as stated above, n-variables areidentified which characterize the tagged material 10, hereinafter termed“features”. Each voxel 2 belonging to each connected region of taggedmaterial has associated with it an n-dimensional vector containing thevalue of the n features for the voxel itself. Examples of these featuresare the intensity of the voxel, the mean intensity and the variance ofthe voxel and of the first neighbouring voxels, for example the first 26voxels, and local homogeneity; likewise, the features may be constitutedby parameters indicating distance from known structures/materials.

Henceforth, said vectors of variables will be termed “feature vectors”.For each voxel 2 of each connected region of tagged material the featurevector associated therewith is therefore obtained. This feature vectoris inputted into a classifier known per se, which establishes to whichclass the corresponding voxel 2 belongs, as described hereinafter.

Advantageously, the classifier provides at the output a plurality ofclasses representing the area of faecal residue 10 a and the area oftissue affected by tagging 10 b.

In this way, specific classification criteria will be available for eachconnected region of tagged material, inasmuch as they are obtained onthe basis of the feature vectors of the voxels 2 present in theconnected region itself. The classification of a voxel 2 is thereforecarried out by comparing the feature vector of the voxel 2 with the datasupplied by the classifier on the basis of the whole of the featurevectors belonging to the individual connected region of tagged material.

The classification criteria for each individual connected region oftagged material are derived, in particular, by inputting the featurevectors of the connected region itself into the classifier, as describedhereinafter. As an alternative, such criteria are obtained both viaanalysis and/or modelling of the distribution of the n-variablesincluded in the feature vector for the voxels 2 belonging to theindividual connected region of tagged material, and via the combinationof rules and hypotheses which combine knowledge a priori regarding thematerials and the tissues present with rules drawn from analysis of thefeature vectors of the individual connected region of tagged material.

An example of a classifier which operates on the basis of the featurevectors inputted distinguishes the voxels 2 belonging to the differentconnected regions of tagged material on the basis of a differentintensity threshold for each region. In this case the feature vector isa scalar, the intensity value, and the classification criterion is anintensity threshold, different for each connected region of taggedmaterial, determined automatically by applying, for example, the Otsumethod to each connected region of tagged material.

In another case, said classifier is constituted by a neural networkhaving a specific structure and with parameters obtained for example by“training” said neural network on each connected region.

Other examples include classifiers of the k-means and fuzzy c-meanstype.

An example of classification obtained via analysis and/or modelling ofthe distribution of the n-variables is that which is obtained byclassifiers of the Bayesian type, which operate on the basis of thedistribution of the parameters of the feature vectors of the voxels 2belonging to the individual connected region of tagged material.

An example of classification obtained via the integration of a prioridata regarding the probability of having predetermined types ofmaterials, if necessary linked to the typology of the individualconnected region of tagged material, is that which is obtained fromclassifiers of the fuzzy type which incorporate specific rules forcarrying out the classification of the voxels 2 of the connected regionin question.

In the following step 106, the recognition of the voxels 2 of the areaof air affected by tagging 8 b is performed. In order to perform saidrecognition 106, starting from each voxel 2 belonging to a contour 15 ofthe area of faecal residue 10 a, for each connected region one or morepaths or radii of exploration are traced, for a predetermined length ofvoxels, preferably 4 voxels. When a voxel 2 is encountered which has anintensity value below a predetermined value, for example equal to −700HU, all the voxels 2 encountered previously along the radius areclassified as belonging to the area of air affected by tagging 8 b,interface between the air present in the lumen of the colon and the areaof faecal residue 10 a, termed air/liquid interface. The surface voxel 2from which the radius has been started is also classified as belongingto the air/liquid interface. In the case where the faeces aresubstantially liquid, it is sufficient to trace the radius in a verticaldirection, inasmuch as liquids always have a horizontal surface, so thatthe air-liquid interface is certainly traversed in a vertical directionif starting from the voxels 2 of the contour 15.

In the following step 108, the re-mapping of the values of the voxels 2of the area of tagged material 10 is carried out in order to attributeto the voxels 2 of the area of faecal residue 10 a intensity valuescompatible with those of the area of pure air 8 a and to correct theintensity of the voxels 2 of the area of tissue affected by tagging 10b, eliminating the effect due to the tagging.

A first method for carrying out this correction is based on the use ofthe data relative to the intensity value, also altered by the tagging,which such voxels 2 of the area of tissue affected by tagging 10 bexhibit. In this case, the correction can be made both by methods thatare known per se and by a new method described hereinafter.

It is necessary to consider the fact that the tagging influences theintensity value of the voxels 2 of the tissue affected by tagging 10 bin contact with the area of faecal residue 10 a. In fact, since thevoxels 2 of the area of faecal residue 10 a have very high intensityvalues, the voxels 2 of the tissue affected by tagging 10 b in contactappear artificially hyperintense. By modelling this relation it ispossible to derive the values of corrected intensity to be substitutedfor the voxels 2 of the tissue affected by tagging 10 b starting fromthe original intensity values of the voxels 2 affected by tagging.Various curves which specify the intensity corrected in dependence onthe original intensity of a tagged voxel are known.

The voxels 2 of the area of faecal residue 10 a are not modelled on thebasis of such curves but are set to values compatible with the area ofpure air 8 a.

For example, in Lakare S., Wan M., Sato M., Kaufman A., “3D DigitalCleansing Using Segmentation Rays”, IEEE Conf. Visualization 2000,37-44, 538, the relation between the original intensity and thecorrected intensity of the voxels 2 of the area of tissue affected bytagging 10 b is tabulated. In another example, Nappi J. and Yoshida H.,“Fully Automated Three-Dimensional Detection of Polyps in Faecal-TaggingCT Colonography”, Acad. Radiol. 2007, 14(3):287-300, the valuescompatible with the surrounding tissue 14 are preserved while the valuesof the voxels 2 in the area of tissue affected by tagging 10 b areobtained by modelling the relation between original voxels and voxelsreconstructed by means of a straight line with negative coefficient.

Similarly, the relation may be modelled with other types of curves, suchas, for example, the sigmoid.

All the methods described above make it possible to obtain areconstruction of the colon wall 13 and a correction of the values ofthe voxels in the area of tissue affected by tagging 10 b, but have thedrawback that the curves which express the relation between the originalintensity and the corrected intensity of the voxels 2 are fixed and donot take into consideration the variation that the tagging may exhibit.Variations in the level of tagging of the faeces in fact cause not onlyvariations in the level of intensity of the voxels 2 of the area offaecal residue 10 a, but also variations in the level of intensity ofthe areas of tissue affected by tagging 10 b, so that the voxels 2 ofthe area of tissue affected by tagging 10 b have an intensity whichdepends on the local concentration of the contrast agent. In cases ofnot very homogeneous tagging, a given intensity encountered in a voxel 2of the area of tissue affected by tagging 10 b may be interpreted bothas caused by a specific proximity to the area of faecal residue 10 a,and as caused by the presence of a specific concentration of thecontrast agent. In these cases, the determination of the correctedintensity values to be substituted depends on the level of tagging foundlocally.

The method according to the invention, compatible also with thesolutions characterized by fixed modelling of the relation betweenoriginal and corrected intensity values of the voxels 2 of the area oftissue affected by tagging 10 b, offers in addition the possibility ofhaving a local evaluation of the intensity value corresponding to thevalue of the area of faecal residue 10 a. This local evaluation may bemade as a result of the classification carried out as described above.

In such a case, for each individual connected region of tagged materialidentified previously, threshold values S1 and S2 are calculated,determined locally both for the area of faecal residue 10 a (thresholdS1), for example by measuring the intensity values of the voxels 2 ofthe area of faecal residue 10 a, and for the surrounding tissue 14(threshold S2), for example by measuring the intensity values of thevoxels 2 of the surrounding tissue 14 adjoining the area of tissueaffected by tagging 10 b. As an alternative, the intensity values of thevoxels 2 of the surrounding tissue 14 may be fixed at a predeterminedvalue, for example, −100 HU. A parametrised curve is then calculated,for example a straight line or a sigmoid, passing through the thresholdvalues S1 and S2, and the original values of the voxels 2 of the area offaecal residue 10 a and of the area of tissue affected by tagging 10 bare corrected with values read from said parametrised curve.

The threshold values S1 and S2 are then calculated as a result of thepresence of the connected regions of tagged material.

Alternatively, in order to obtain a more rapid procedure from thecomputing point of view, a single threshold value is selected for thearea of faecal residue 10 a, being based on information provided by astatistical summary, such as for example the mean, the mode or themedian, of the whole of the intensity values of the areas of faecalresidue 10 a belonging to all the connected regions of tagged material.

A second method for carrying out the correction of the intensity of thevoxels 2 of the area of tissue affected by tagging 10 b, eliminating theeffect due to the tagging, comprises, as a first step, attributing tothe voxels 2 of the area of faecal residue 10 a a predeterminedintensity value equal to that of the area of pure air 8 a. In thefollowing description, the area of faecal residue 10 a will be indicatedas corrected area 10 a′ (see FIG. 5).

The intensity values of the voxels 2 of the area of tissue affected bytagging 10 b are modified in order to restore values compatible with theintensity value of the voxels 2 of the surrounding tissue 14 and of thecorrected area 10 a′. In order to carry out this modification correctlyit is however necessary to remember that the intensity values of thevoxels 2 of the area of tissue affected by tagging 10 b, because of thepartial volume effect derived from the proximity of the voxels 2 of thecorrected area 10 a′, must be rendered hypointensive artificially, sincethe voxels 2 of surrounding tissue 14, when they are in contact with airwhich has a low attenuation, exhibit hypointense values. This partialvolume effect, although still an artefact, must be replicated in orderto reproduce the normal appearance of the mucosa of the colon.

In order to attribute the intensity values correctly to the voxels 2 ofthe area of tissue affected by tagging 10 b, incorporating wherenecessary the alteration of the values caused by the presence of thevoxels 2 of the corrected area 10 a′, in this case the data relating tothe location of the voxels 2 of the area of tissue affected by tagging10 b are used and the fact that the intensity value of a voxel 2 isinfluenced by the value of the neighbouring voxels 2 is explicitly takeninto consideration.

The voxels 2 of the area of tissue affected by tagging 10 b aresub-divided into a first sub-area 10 b′ and into a second sub-area 10 b″(see FIG. 4) on the basis of the distance from the voxels 2 of thecorrected area 10 a′. In particular, the voxels 2 of the area of tissueaffected by tagging 10 b located at a distance from the voxels 2 of thecorrected area 10 a′ less than, for example, 4 voxels are assigned tothe first sub-area 10 b′, and the other voxels 2 to the second sub-area10 b″.

An intensity value which takes account of the partial volume effectdetermined by the proximity of the voxels 2 of the corrected area 10 a′is assigned to the voxels 2 of the first sub-area 10 b′, as describedhereinafter.

An intensity value compatible with the intensity value of the voxels 2of the surrounding tissue 14, for example −100 HU, is assigned to thevoxels 2 of the second sub-area 10 b″.

It is known that in the transition between the corrected area 10 a′ andthe colon wall 13 the intensity follows an increasing course, as shownin the example in FIG. 5.

In order to reconstruct the effect due to the presence of the correctedarea 10 a′, the transition between said corrected area 10 a′ and thecolon wall 13 is modelled and the intensity values of the voxels 2 ofthe first sub-area 10 b′ are substituted by predetermined values definedby the model, as will now be described.

A first method of reconstruction consists in substituting for the voxels2 of the first sub-area 10 b′ predetermined values obtained byobservation of a predetermined number, for example 50, of transitionzones of this type. Advantageously, the intensity value of the voxels 2of the first sub-area 10 b′ which are located at a distance of one voxelfrom the voxels 2 of the corrected area 10 a′ is set at −600 HU, theintensity value is set at −350 HU if said distance is equal to twovoxels, and is set at −200 HU if said distance is three voxels.

A second method of reconstruction consists in using curves whichrepresent how the intensity values of the voxels 2 of the first sub-area10 b′ change as the distance from the corrected area 10 a′ increases.The parametrisation of the curves takes place on the basis of theintensity values of the voxels 2 of the corrected area 10 a′ and of thesurrounding tissue 14 in the interface zone which is being processed.Preferably, the effect produced by the presence of the corrected area 10a′ is exerted on 3 neighbouring voxels.

By using as abscissa the values for the distance of the voxels 2 fromthe corrected area 10 a′ and as ordinate the intensity values of thevoxels 2 detected above, a straight line 600 is constructed (see FIG.6), which starts from the point A(0,−900), where 0 is the distance invoxels from the corrected area 10 a′ and −900 HU is the intensity of thecorrected area 10 a′, and reaches the point T(4,−100), where 4 is thedistance in voxels from the corrected area 10 a′ and −100 HU is theintensity value of the surrounding tissue 14.

Alternatively, in order to define the intensity value of the surroundingtissue 14, all the voxels 2 close to the voxels 2 of the second sub-area10 b″ are detected, excluding the voxels 2 which have intensity valuescompatible with the corrected area 10 a′, with the area of pure air 8 aor with the first sub-area 10 b′, and the mean of the intensity valuesis calculated. Advantageously, this exclusion is performed by usingpreset thresholds, i.e. voxels 2 having intensity values below −200 HUand above 150 HU are excluded.

At this point, the original values of the voxels 2 of the first sub-area10 b′ are corrected with values read from said straight line. In FIG. 6,the points I, II and III identify which intensity values to attribute tothe voxels 2 of the first sub-area 10 b′ which are located respectivelyat a distance of one, two and three voxels from the corrected area 10a′.

This same method may likewise be applied using other curves whichapproximate closer to the air-tissue transition, such as, for example,the sigmoid curve.

Finally, in step 110 the correction of the values of the voxels 2 of thearea of air affected by tagging 8 b is carried out. A value compatiblewith the value of the air, preferably equal to −900 HU, is assigned tothese voxels 2.

The method according to the invention is carried out by a system of thetype illustrated in FIG. 7, which comprises a computerised work station500, of known type, having a processing sub-system 510, a display 520, akeyboard 530, a mouse 540 and a device for connection to a local network(network bus) 550. Alternatively, the processing system may be of thedistributed type (not illustrated), having a processing sub-system andinput/output, local or remote peripherals. The work station 500 or thedistributed system are arranged for processing groups or modules ofprocessing and calculating programs stored on disk 560 or networkaccessible, suitable for displaying the method described, and fordisplaying the results on the display 520. The solutions referred toherein are regarded as well known in the art and will not be describedfurther herein since they are not relevant per se for the purposes ofimplementation and understanding of the present invention.

Naturally, the principle of the invention remaining the same, the formsof embodiment and details of construction may be varied widely withrespect to those described and illustrated purely by way of non-limitingexample, without thereby departing from the scope of protection of thepresent invention defined by the attached claims.

1-18. (canceled)
 19. A method of classification of portions of imagescorresponding to faecal residues from a tomographic image of acolorectal region, said image comprising a plurality of voxels (2) eachhaving a predetermined intensity value, said image showing. at least oneportion of colon (6 a, 6 b, 6 c, 6 d) comprising: at least one area oftagged material (10) comprising at least one area of faecal residue (10a) corresponding to tagged faecal residues present in the colon and atleast one area of tissue affected by tagging (10 b) corresponding totissues of the colon located in contact with the faecal residues; atleast one area of air (8) corresponding to the air contained in thecolon comprising an the area of pure air (8 a) not influenced by thefaecal residues; the method comprising the operations of: identifying(100), on the basis of a predetermined criterion of identification basedon the intensity values, first connected regions comprising connectedvoxels (2) having an intensity value exceeding a threshold; identifying,within the said first connected regions, a plurality of connectedregions of tagged material comprising voxels (2) representing the areaof tagged material (10); classifying (104) each voxel (2) of the saidplurality of connected regions of tagged material on the basis ofclassification criteria specific for each of said plurality of connectedregions of tagged material, in such a way as to identify voxels (2)corresponding to said area of faecal residue (10 a) and voxels (2)corresponding to said area of tissue affected by tagging (10 b).
 20. Themethod of classification according to claim 19, wherein the operation ofidentifying (100) the said first connected regions of connected voxels(2) having an intensity value exceeding a threshold comprises the stepsof: selecting starting voxels (16) having an intensity value equal to orgreater than a first predetermined threshold; generating first connectedregions comprising voxels (2) connected to said starting voxels (16) andhaving an intensity value greater than a second predetermined threshold.21. The method of classification according to claim 19, wherein theoperation of classifying (104) each voxel (2) of the said plurality ofconnected regions of tagged material comprises the steps of: identifyingat least one image parameter which characterizes the area of taggedmaterial (10); associating to each voxel (2) of each of said pluralityof connected regions of tagged material a feature vector containing thevalue of said at least one image parameter for the given voxel (2);processing, for each of said plurality of connected regions of taggedmaterial and by means of automatic classification means, all the featurevectors associated to the voxels (2) belonging to each connected regionof tagged material, thus obtaining a plurality of classificationcriteria specific for each of said plurality of connected regions oftagged material; classifying each voxel (2) by comparing the featurevector associated to the voxel (2) with the respective specificclassification criterion, in order to identify voxels (2) correspondingto the area of faecal residue (10 a) and voxels (2) corresponding to thearea of tissue affected by tagging (10 b).
 22. The method ofclassification according to claim 19 wherein, in the case where theimage comprises also portions of bone (12), the method further comprisesthe operations of: applying morphological operations to the firstconnected regions so as to obtain a plurality of connected regions ofbone comprising voxels (2) representing the portions of bone (12);identifying said connected regions of bone, distinguishing them from theconnected regions of tagged material, on the basis of known dataregarding the extent, the shape or the location of the portions of bone(12).
 23. The method of classification according to claim 19, in thecase where the image comprises also an area of air affected by tagging(8 b) influenced method by the faecal residues, the method furthercomprises the operation of recognising (106), starting from each of saidfirst connected regions, voxels (2) representing the area of airaffected by tagging (8 b).
 24. The method of classification according toclaim 23, wherein the operation of recognising (106) voxels (2)representing the area of air affected by tagging (8 b) comprises thesteps of defining a contour (15) of the area of faecal residue (10 a);tracing, for each voxel (2) belonging to said contour (15), at least onepath of exploration for a predetermined length until a voxel (2) havingan intensity value below a preset value is reached
 25. A method ofelectronic subtraction of faecal residues from a tomographic image of acolorectal region, comprising the operations of: carrying out the methodof classification according to claim 19; attributing, in each of saidplurality of connected regions, to the voxels (2) corresponding to thearea of faecal residue (10 a), intensity values compatible with theintensity values of the voxels (2) of the area of pure air (8 a);correcting, in each of said plurality of connected regions, theintensity of the voxels (2) of the area of tissue affected by tagging(10 b) by substituting predetermined correction values based on apredetermined model of a partial volume effect for the intensity valuesof said voxels (2).
 26. The method of electronic subtraction accordingto claim 25, wherein the operation of attributing to voxels (2)corresponding to the area of faecal residue (10 a) intensity valuescompatible with the intensity values of the voxels (2) of the area ofpure air (8 a) comprises the step of: substituting for the intensityvalues of the voxels (2) of the area of faecal residue (10 a) apredetermined value.
 27. The method of electronic subtraction accordingto claim 25, wherein the operation of correcting the intensity of thevoxels (2) of the area of tissue affected by tagging (10 b) comprisesthe step of: substituting for the intensity values of the voxels (2) ofthe area of tissue affected by tagging (10 b) correction values obtainedfrom a predetermined modelling curve representing said correction valueas a function of the intensity values of the voxels (2) of the area oftissue affected by tagging (10 b).
 28. The method according to claim 25,wherein the operations of attributing to the voxels (2) of the area offaecal residue (10 a) intensity values compatible with the intensityvalues of the voxels (2) of the area of pure air (8 a) and of correctingthe intensity of the voxels (2) of the area of tissue affected bytagging (10 b) comprise the steps of: a) calculating a first thresholdintensity value (S1) on the basis of the intensity values of the voxels(2) of the area of faecal residue (10 a); b) calculating a parametrisedcurve passing through said first threshold value (S1), said parametrisedcurve representing correction values for the voxels (2) of the area oftissue affected by tagging (10 b) as a function of the intensity valuesof said voxels (2); c) substituting for the intensity values of thevoxels (2) of the area of faecal residue (10 a) and of the area oftissue affected by tagging (10 b) correction values obtained from saidparametrised curve.
 29. The method of electronic subtraction accordingto claim 28, comprising, in the case where the image also comprises anarea of tissue (14) surrounding said at least one portion of colon (6 a,6 b, 6 c), the operation of: d) calculating a second threshold intensityvalue (S2) by measuring the intensity values of the voxels (2) of thearea of surrounding tissue (14) adjoining the area of tissue affected bytagging (10 b); and wherein the step b) is substituted by the followingoperation: calculating a parametrised curve passing through said first(S1) and said second (S2) threshold value, said parametrised curverepresenting correction values for the voxels (2) of the area of tissueaffected by tagging (10 b) as a function of the intensity values of saidvoxels (2).
 30. The method of electronic subtraction according to claim29, wherein the operation d) is substituted by the following operation:substituting for the intensity values of the voxels (2) of the area ofsurrounding tissue (14) a predetermined value.
 31. The method ofelectronic subtraction according to claim 25, wherein the operation ofcorrecting the intensity of the voxels (2) of the area of tissueaffected by tagging (10 b) comprises the step of sub-dividing the voxels(2) of the area of tissue affected by tagging (10 b) into a firstsub-area (10 b) and a second sub-area (10 b″) in dependence on thedistance from the voxels (2) of the area of faecal residue (10 a);substituting for the voxels (2) of the first sub-area (10 b) firstpredetermined values; substituting for the voxels (2) of the secondsub-area (10 b″) second predetermined values.
 32. The method ofelectronic subtraction according to claim 25, wherein the operation ofcorrecting the intensity of the voxels (2) of the area of tissueaffected by tagging (10 b) comprises the step of subdividing the voxels(2) of the area of tissue affected by tagging (10 b) into a firstsub-area (10 b) and a second sub-area (10 b″) in dependence on thedistance from the voxels (2) of the area of faecal residue (10 a);calculating a parametrised curve which represents the variations inintensity of the voxels (2) of the first sub-area (10 b′) in dependenceon the distance from the voxels (2) of the area of faecal residue (10a); substituting for the intensity values of the voxels (2) of the firstsub-area (10 b′) correction values obtained from said parametrisedcurve; substituting for the voxels (2) of the second sub-area (10 b″)second predetermined values.
 33. A system of classification suitable forcarrying out the method of classification as claimed in any one ofclaims
 19. 34. A system of electronic subtraction suitable for carryingout the method of electronic subtraction as claimed in any one of claims25.
 35. A processing program or group of programs that can be carriedout by a processing system described in claim 15 and comprising one ormore code modules for the implementation of a method of classificationaccording to any one of claim
 19. 36. A processing program or group ofprograms that can be carried out by a processing system described inclaim 16 and comprising one or more code modules for the implementationof a method of electronic subtraction according to any one of claims 25.